Carreau/Ben.ipynb

## Ben.ipynb

      
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## data-numpy-32ed43dfae87a2d65bc67004df279a7d3465ae34.json
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## data-numpy-cdfbdf428d9df9c7119cecae323512a4cd3f57b7.json
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"cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.002963042352348566, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.002922666259109974, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.002908542286604643, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.0029194168746471405, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.0029102088883519173, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.0029189996421337128, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.002901333849877119, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.0029067909345030785, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.0029077911749482155, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.0028946250677108765, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.0029785833321511745, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.002885458990931511, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.0029226248152554035, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.002913875039666891, "eq_nan": false, "git_rev": 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"cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.002878416795283556, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.0028727082535624504, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.002881458029150963, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.0028813332319259644, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.002902541309595108, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.0029092920012772083, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.0029048342257738113, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.0029083327390253544, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.0028989999555051327, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.0029077501967549324, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}, {"key": "float_array-float_array_2", "timing": 0.0029000421054661274, "eq_nan": false, "git_rev": "cdfbdf428d9df9c7119cecae323512a4cd3f57b7"}]

## perf.ipy
import numpy as np
import numpy
import seaborn as sns
import pandas as pd
import json
import matplotlib.pyplot as plt

base = np.random.randint(1, 100, (1000, 1000), dtype='int64')
a = base.copy()
a1 = base.copy()

b = base.copy().astype('float')
b[b == 50] = np.nan

b1 = b.copy()

data = {'int_array':a, 'float_array':b, 'int_array_2':a1, 'float_array_2': b1}
timings = {}
for (x, y) in [('int_array','int_array'),('int_array','int_array_2'),('float_array','float_array'),('float_array','float_array_2')]:
    ax = data[x]
    bx = data[y]
    for en in [True, False]:
        key = (x,y,en)
        res = np.array_equal(ax, bx, equal_nan=en)
        print(f"np.array_equal({x}, {y}, equal_nan={en}) == {res}")
        timing = %timeit -n10 -r100 -o np.array_equal(ax, bx, equal_nan=en)
        timings[key] = timing


data2 = []
for (x,y,en),v in timings.items():
    for t in v.all_runs:
        data2.append({'key':f"{x}-{y}", 'timing':t, 'eq_nan':en, 'git_rev':numpy.version.git_revision})

rev = numpy.version.git_revision
with open(f'data-numpy-{rev}.json', 'w') as f:
    f.write(json.dumps(data2))
	import numpy as np
	import numpy
	import seaborn as sns
	import pandas as pd
	import json
	import matplotlib.pyplot as plt

	base = np.random.randint(1, 100, (1000, 1000), dtype='int64')
	a = base.copy()
	a1 = base.copy()

	b = base.copy().astype('float')
	b[b == 50] = np.nan

	b1 = b.copy()

	data = {'int_array':a, 'float_array':b, 'int_array_2':a1, 'float_array_2': b1}
	timings = {}
	for (x, y) in [('int_array','int_array'),('int_array','int_array_2'),('float_array','float_array'),('float_array','float_array_2')]:
	ax = data[x]
	bx = data[y]
	for en in [True, False]:
	key = (x,y,en)
	res = np.array_equal(ax, bx, equal_nan=en)
	print(f"np.array_equal({x}, {y}, equal_nan={en}) == {res}")
	timing = %timeit -n10 -r100 -o np.array_equal(ax, bx, equal_nan=en)
	timings[key] = timing


	data2 = []
	for (x,y,en),v in timings.items():
	for t in v.all_runs:
	data2.append({'key':f"{x}-{y}", 'timing':t, 'eq_nan':en, 'git_rev':numpy.version.git_revision})

	rev = numpy.version.git_revision
	with open(f'data-numpy-{rev}.json', 'w') as f:
	f.write(json.dumps(data2))